Evidence map›Paper›PMID 40697956›Full record

ArticleFrontiers in neuroinformatics2025

Decoding event-related potentials: single-dose energy dietary supplement acts on earlier brain processes than we thought.

Karina J Maciejewska

Abstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Karina J MaciejewskaInstitute of Biomedical Engineering, Faculty of Science and Technology, University of Silesia in Katowice, Katowice, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This paper describes an experimental work using machine learning (ML) as a "decoding for interpretation" to understand the brain's physiology better. Methods: Multivariate pattern analysis (MVPA) was used to decode the patterns of event-related potentials (ERPs, brain responses to stimuli) in a visual oddball task. The ERPs were measured before (run 1) and after (30 min-run 2, 90 min-run 3) a single dose of an energy dietary supplement with only a small amount of caffeine. Results: Its effect on ERPs was successfully decoded. Above-chance decoding accuracies were obtained between ∼350 and 450 ms (corresponds to P3 peak) after stimulus onset for both the placebo and study groups, whereas between ∼200 and 260 ms (corresponds to P2 waveform) only in the placebo group. Moreover, the decoding accuracies were significantly higher in the placebo than in the study group in the 200-250 ms and 450-500 ms time bins. Our previously reported findings showed an increase in P3 amplitude among the runs only in the placebo group, indicating a reduction of mental fatigue caused by the supplementation. Discussion: Thus, this paper extends these results, showing that the dietary supplement affected the brain's neural activity related to the attention-related processing of the visual stimuli in the oddball task already at the early processing stage. This implies that inhibiting the fatigue-related brain changes after only a single dose of a dietary neurostimulant acts on early and late processing stages. This emphasizes the value of decoding for interpretation in ERP research. The results also point out the necessity of controlling the uptake of dietary supplements before the neurophysiological examinations.

Indexed as

attentiondecoding brain’s functionEEGevent-related potentialsmultivariate pattern analysis

Identifiers

PMID40697956
PMCPMC12279804

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.